Lead Gen AI/Data Science Engineer

Responsibilities

• Architect and design production-ready applications leveraging Generative AI and agentic AI frameworks.
• Build, deploy, and monitor intelligent workflows using Large Language Models (LLMs), multi-agent coordination, and orchestration pipelines.
• Integrate new AI applications with traditional pre-existing applications.
• Implement prompt engineering strategies, retrieval-augmented generation (RAG), and contextual memory systems.
• Implement various AI coding techniques (context-driven, spec-driven development).
• Arcitect and implement AI agents capable of reasoning, planning, and multi-step task execution.
• Implement and utilize Model Context Protocol (MCP) patterns to enable structured communication between agents, tools, and systems.
• Provide mentoring and technical guidance to developers and other AI Engineers.
• Develop evaluation pipelines to measure accuracy, safety, and performance of AI systems.
• Optimize latency, cost efficiency, and scalability of AI-powered workflows.
• Collaborate with product managers, data teams, and software engineers to deliver AI features.
• Ensure reliability through testing, logging, monitoring, and observability of AI behavior.
• Own architectural decisions, coding standards, and best practices.
• Act as an AI trailblazer for development teams by promoting best practices, reusable patterns, and adopting AI-driven engineering approaches.
• Apply AI governance principles to ensure responsible model usage, auditability, and transparency in AI workflows.
• Support implementation of governance controls related to data handling, model behavior monitoring, and risk mitigation.
• Research emerging AI tools and frameworks to continuously improve system capabilities.
• Document AI workflows and maintain reproducible engineering processes.

Qualifications

• Strong software engineering foundations and hands-on experience in Generative and Agentic AI.